Pub Date : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313094
Ofelia P. Villarreal, Kareth León, D. Espinosa, W. Agudelo, H. Arguello
Seismic survey acquisition permits capturing subsurface data by sensing the seismic waves induced by an artificial source. Hundreds of kilometers are sensed at a sampling rate that satisfies the Nyquist/Shannon theorem to avoid signal aliasing, this means that a high-density arrangement of sensors is required. In seismic, a compressive seismic imaging (CSI) framework has been developed. To test CS theory, random sampling or simultaneous shooting techniques are applied to marine and land environments. For land, random acquisitions require creating new paths on the surface to place each source and receiver, additionally, for terrains with complex access, the artificial used sources are made of dynamite. For this reason, random acquisitions have an elevated environmental impact compared to regular acquisitions, where the same path is used to locate all the sources. This work proposes to use regular sampling (which is not a traditional sampling technique to be used with CS concepts) and to remove sources in a specific configuration present in orthogonal grids with CS concepts in order to reduce acquisition costs and environmental impact. The seismic wave data that should be induced by the removed source is reconstructed using a proposed modified iterative hard thresholding (IHT) algorithm that favors structural similarities of the data. Simulations were performed on real data to illustrate the accuracy of the proposed method, using the Curvelet transformation basis, which attains reconstructions 50% faster than Wavelets.
{"title":"Compressive sensing seismic acquisition by using regular sampling in an orthogonal grid","authors":"Ofelia P. Villarreal, Kareth León, D. Espinosa, W. Agudelo, H. Arguello","doi":"10.1109/CAMSAP.2017.8313094","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313094","url":null,"abstract":"Seismic survey acquisition permits capturing subsurface data by sensing the seismic waves induced by an artificial source. Hundreds of kilometers are sensed at a sampling rate that satisfies the Nyquist/Shannon theorem to avoid signal aliasing, this means that a high-density arrangement of sensors is required. In seismic, a compressive seismic imaging (CSI) framework has been developed. To test CS theory, random sampling or simultaneous shooting techniques are applied to marine and land environments. For land, random acquisitions require creating new paths on the surface to place each source and receiver, additionally, for terrains with complex access, the artificial used sources are made of dynamite. For this reason, random acquisitions have an elevated environmental impact compared to regular acquisitions, where the same path is used to locate all the sources. This work proposes to use regular sampling (which is not a traditional sampling technique to be used with CS concepts) and to remove sources in a specific configuration present in orthogonal grids with CS concepts in order to reduce acquisition costs and environmental impact. The seismic wave data that should be induced by the removed source is reconstructed using a proposed modified iterative hard thresholding (IHT) algorithm that favors structural similarities of the data. Simulations were performed on real data to illustrate the accuracy of the proposed method, using the Curvelet transformation basis, which attains reconstructions 50% faster than Wavelets.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"32 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120824426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313131
J. Galy, É. Chaumette, F. Vincent
In deterministic parameters estimation, it is common place to design a minimum variance distortionless response estimator (MVDRE) instead of a maximum likelihood estimator to tackle the problem of identifying the components of observations formed from a linear superposition of individual signals to noisy data. When several observations are available and the individual signals are allowed to perform a random walk between observations, one obtains the general class of linear discrete state-space models. This paper introduces a novel recursive formulation of the MVDREs of individual signals compatible with recursive estimation.
{"title":"A general class of recursive minimum variance distortionless response estimators","authors":"J. Galy, É. Chaumette, F. Vincent","doi":"10.1109/CAMSAP.2017.8313131","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313131","url":null,"abstract":"In deterministic parameters estimation, it is common place to design a minimum variance distortionless response estimator (MVDRE) instead of a maximum likelihood estimator to tackle the problem of identifying the components of observations formed from a linear superposition of individual signals to noisy data. When several observations are available and the individual signals are allowed to perform a random walk between observations, one obtains the general class of linear discrete state-space models. This paper introduces a novel recursive formulation of the MVDREs of individual signals compatible with recursive estimation.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"730 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134208603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313128
Willem J. Marais, R. Willett
This paper considers the denoising and reconstruction of images corrupted by Poisson noise. Poisson noise arises in the context of counting the emission or scattering of photons. In various application domains, such as astronomy and medical imaging, photons counts are low resulting in very low signal-to-noise ratio images. Recently, Azzari and Foi investigated using BM3D for Poisson image denoising in a coarse-to-fine image resolution framework. Specifically, the denoised result at a coarse resolution is used to improve the denoising of the next finer resolution, resulting in state-of-the-art denoising results. This paper presents an alternative regularized maximum likelihood formulation of the reconstruction problem, and explains how it can be solved using a coarse-to-fine proximal gradient optimization algorithm. The proposed methods of this paper are compared to the methods of Azzari and Foi, highlighting their strong similarities. The advantage of the proposed method of this paper is that it easily generalizes to inverse problem settings, which is demonstrated in the context of denoising a Poisson noisy image with missing pixels (i.e. image inpainting); in contrast there is no known generalization of the coarse-to-fine BM3D denoising method that was proposed by Azzari and Foi.
{"title":"Proximal-Gradient methods for poisson image reconstruction with BM3D-Based regularization","authors":"Willem J. Marais, R. Willett","doi":"10.1109/CAMSAP.2017.8313128","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313128","url":null,"abstract":"This paper considers the denoising and reconstruction of images corrupted by Poisson noise. Poisson noise arises in the context of counting the emission or scattering of photons. In various application domains, such as astronomy and medical imaging, photons counts are low resulting in very low signal-to-noise ratio images. Recently, Azzari and Foi investigated using BM3D for Poisson image denoising in a coarse-to-fine image resolution framework. Specifically, the denoised result at a coarse resolution is used to improve the denoising of the next finer resolution, resulting in state-of-the-art denoising results. This paper presents an alternative regularized maximum likelihood formulation of the reconstruction problem, and explains how it can be solved using a coarse-to-fine proximal gradient optimization algorithm. The proposed methods of this paper are compared to the methods of Azzari and Foi, highlighting their strong similarities. The advantage of the proposed method of this paper is that it easily generalizes to inverse problem settings, which is demonstrated in the context of denoising a Poisson noisy image with missing pixels (i.e. image inpainting); in contrast there is no known generalization of the coarse-to-fine BM3D denoising method that was proposed by Azzari and Foi.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130865149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313064
Ahmed S. Zamzam, Xiao Fu, E. Dall’Anese, N. Sidiropoulos
The AC Optimal Power Flow (OPF) is a core optimization task in the domain of power system operations and control. It is known to be nonconvex (and, in fact, NP-hard). In general operational scenarios, identifying feasible (let alone optimal) power-flow solutions remains hard. This paper leverages the recently proposed Feasible Point Pursuit algorithm for solving the OPF problem to devise a fully distributed procedure that can identify AC OPF solutions. The paper considers a multi-area setting and develops an algorithm where all the computations are done locally withing each area, and then the local controllers have to communicate to only their neighbors a small amount of information pertaining to the boundary buses. The merits of the proposed approach are illustrated through an example of a challenging transmission network.
{"title":"Distributed optimal power flow using feasible point pursuit","authors":"Ahmed S. Zamzam, Xiao Fu, E. Dall’Anese, N. Sidiropoulos","doi":"10.1109/CAMSAP.2017.8313064","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313064","url":null,"abstract":"The AC Optimal Power Flow (OPF) is a core optimization task in the domain of power system operations and control. It is known to be nonconvex (and, in fact, NP-hard). In general operational scenarios, identifying feasible (let alone optimal) power-flow solutions remains hard. This paper leverages the recently proposed Feasible Point Pursuit algorithm for solving the OPF problem to devise a fully distributed procedure that can identify AC OPF solutions. The paper considers a multi-area setting and develops an algorithm where all the computations are done locally withing each area, and then the local controllers have to communicate to only their neighbors a small amount of information pertaining to the boundary buses. The merits of the proposed approach are illustrated through an example of a challenging transmission network.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131329937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313059
B. Kailkhura, P. Ray, D. Rajan, A. Yen, P. Barnes, R. Goldhahn
In this paper, we propose a locally optimum detection (LOD) scheme for detecting a weak radioactive source buried in background clutter. We develop a decentralized algorithm, based on alternating direction method of multipliers (ADMM), for implementing the proposed scheme in autonomous sensor networks. Results show that algorithm performance approaches the centralized clairvoyant detection algorithm in the low SNR regime, and exhibits excellent convergence rate and scaling behavior (w.r.t. number of nodes). We also devise a low-overhead, robust ADMM algorithm for Byzantine-resilient detection, and demonstrate its robustness to data falsification attacks.
{"title":"Byzantine-Resilient locally optimum detection using collaborative autonomous networks","authors":"B. Kailkhura, P. Ray, D. Rajan, A. Yen, P. Barnes, R. Goldhahn","doi":"10.1109/CAMSAP.2017.8313059","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313059","url":null,"abstract":"In this paper, we propose a locally optimum detection (LOD) scheme for detecting a weak radioactive source buried in background clutter. We develop a decentralized algorithm, based on alternating direction method of multipliers (ADMM), for implementing the proposed scheme in autonomous sensor networks. Results show that algorithm performance approaches the centralized clairvoyant detection algorithm in the low SNR regime, and exhibits excellent convergence rate and scaling behavior (w.r.t. number of nodes). We also devise a low-overhead, robust ADMM algorithm for Byzantine-resilient detection, and demonstrate its robustness to data falsification attacks.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114451415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313194
Zhe Zhang, Z. Tian
PhaseLift is a noted convex optimization technique for phase retrieval that can recover a signal exactly from amplitude measurements only, with high probability. Conventional PhaseLift requires a relatively large number of samples that sometimes can be costly to acquire. This paper focuses on some practical applications where the signal of interest is composed of a few Vandermonde components, such as line spectra. A novel phase retrieval framework, namely ANM-PhaseLift, is developed that exploits the Vandermonde structure to alleviate the sampling requirements. Specifically, the atom set of amplitude-based quadratic measurements is identified, and atomic norm minimization (ANM) is introduced into PhaseLift to considerably reduce the number of measurements that are needed for accurate phase retrieval. The benefit of ANM-PhaseLift is particularly attractive in applications where the Vandermonde structure is presented, such as massive MIMO and radar imaging.
{"title":"ANM-PhaseLift: Structured line spectrum estimation from quadratic measurements","authors":"Zhe Zhang, Z. Tian","doi":"10.1109/CAMSAP.2017.8313194","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313194","url":null,"abstract":"PhaseLift is a noted convex optimization technique for phase retrieval that can recover a signal exactly from amplitude measurements only, with high probability. Conventional PhaseLift requires a relatively large number of samples that sometimes can be costly to acquire. This paper focuses on some practical applications where the signal of interest is composed of a few Vandermonde components, such as line spectra. A novel phase retrieval framework, namely ANM-PhaseLift, is developed that exploits the Vandermonde structure to alleviate the sampling requirements. Specifically, the atom set of amplitude-based quadratic measurements is identified, and atomic norm minimization (ANM) is introduced into PhaseLift to considerably reduce the number of measurements that are needed for accurate phase retrieval. The benefit of ANM-PhaseLift is particularly attractive in applications where the Vandermonde structure is presented, such as massive MIMO and radar imaging.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132918693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313127
Yanning Shen, Panagiotis A. Traganitis, G. Giannakis
In this era of data deluge, many signal processing and machine learning tasks are faced with high-dimensional datasets, including images, videos, as well as time series generated from social, commercial and brain network interactions. Their efficient processing calls for dimensionality reduction techniques capable of properly compressing the data while preserving task-related characteristics, going beyond pairwise data correlations. The present paper puts forth a nonlinear dimensionality reduction framework that accounts for data lying on known graphs. The novel framework turns out to encompass most of the existing dimensionality reduction methods as special cases, and it is capable of capturing and preserving possibly nonlinear correlations that are ignored by linear methods, as well as taking into account information from multiple graphs. An efficient algorithm admitting closed-form solution is developed and tested on synthetic datasets to corroborate its effectiveness.
{"title":"Nonlinear dimensionality reduction on graphs","authors":"Yanning Shen, Panagiotis A. Traganitis, G. Giannakis","doi":"10.1109/CAMSAP.2017.8313127","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313127","url":null,"abstract":"In this era of data deluge, many signal processing and machine learning tasks are faced with high-dimensional datasets, including images, videos, as well as time series generated from social, commercial and brain network interactions. Their efficient processing calls for dimensionality reduction techniques capable of properly compressing the data while preserving task-related characteristics, going beyond pairwise data correlations. The present paper puts forth a nonlinear dimensionality reduction framework that accounts for data lying on known graphs. The novel framework turns out to encompass most of the existing dimensionality reduction methods as special cases, and it is capable of capturing and preserving possibly nonlinear correlations that are ignored by linear methods, as well as taking into account information from multiple graphs. An efficient algorithm admitting closed-form solution is developed and tested on synthetic datasets to corroborate its effectiveness.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133358220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313179
Edwin Vargas, H. Arguello, J. Tourneret
This work aims at reconstructing a high-spatial high-spectral image from the complementary information provided by sensors that allow us to acquire compressive measurements of different spectral ranges and different spatial resolutions, such as hyperspectral (HS) and multi-spectral (MS) compressed images. To solve this inverse problem, we investigate a new optimization algorithm based on the linear spectral unmixing model and using a block coordinate descent strategy. The non-negative and sum to one constraints resulting from the intrinsic physical properties of abundance and a total variation penalization are used to regularize this ill-posed inverse problem. Simulations results conducted on realistic compressive hyperspectral and multispectral images show that the proposed algorithm can provide fusion and unmixing results that are very close to those obtained when using uncompressed images, with the advantage of using a significant reduced number of measurements.
{"title":"Spectral image fusion from compressive measurements using spectral unmixing","authors":"Edwin Vargas, H. Arguello, J. Tourneret","doi":"10.1109/CAMSAP.2017.8313179","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313179","url":null,"abstract":"This work aims at reconstructing a high-spatial high-spectral image from the complementary information provided by sensors that allow us to acquire compressive measurements of different spectral ranges and different spatial resolutions, such as hyperspectral (HS) and multi-spectral (MS) compressed images. To solve this inverse problem, we investigate a new optimization algorithm based on the linear spectral unmixing model and using a block coordinate descent strategy. The non-negative and sum to one constraints resulting from the intrinsic physical properties of abundance and a total variation penalization are used to regularize this ill-posed inverse problem. Simulations results conducted on realistic compressive hyperspectral and multispectral images show that the proposed algorithm can provide fusion and unmixing results that are very close to those obtained when using uncompressed images, with the advantage of using a significant reduced number of measurements.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132593182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313149
A. K. Dey, Y. Gel, H. Poor
A new method for intentional islanding of power grids is proposed, based on a data-driven and inherently geometric concept of data depth. The utility of the new depth-based islanding is illustrated in application to the Italian power grid. It is found that spectral clustering with data depths outperforms spectral clustering with k-means in terms of k-way expansion. Directions on how the k-depths can be extended to multilayer grids in a tensor representation are outlined.
{"title":"Intentional islanding of power grids with data depth","authors":"A. K. Dey, Y. Gel, H. Poor","doi":"10.1109/CAMSAP.2017.8313149","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313149","url":null,"abstract":"A new method for intentional islanding of power grids is proposed, based on a data-driven and inherently geometric concept of data depth. The utility of the new depth-based islanding is illustrated in application to the Italian power grid. It is found that spectral clustering with data depths outperforms spectral clustering with k-means in terms of k-way expansion. Directions on how the k-depths can be extended to multilayer grids in a tensor representation are outlined.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125563638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313134
Razgar Rahimi, S. Shahbazpanahi
We consider a single-carrier asynchronous relay network, where two transceivers wish to communicate with the help of multiple multi-antenna relays. In an asynchronous network, the signal transmitted by any of the two transceivers arrives at different relays with significantly different delays. Similarly, the signals forwarded by different relays arrive at the receiver frontend of any of the two transceivers with different delays. In such a network, aiming to minimize the total transmit power consumed in the entire network, we obtain the relay beamforming matrices and the transceivers' transmit powers such that two given date rates at the two transceivers are to be satisfied. We develop a model for the end-to-end channel and use this model to address the network beamforming problem. Assuming symmetric relay beamforming matrices, we present a computationally efficient solution to this problem. The numerical results show that for a given total number of antennas, there is an optimal number of antennas per relays which results in the lowest total power consumption in the entire network.
{"title":"Network beamforming for asynchronous MIMO two-way relay networks","authors":"Razgar Rahimi, S. Shahbazpanahi","doi":"10.1109/CAMSAP.2017.8313134","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313134","url":null,"abstract":"We consider a single-carrier asynchronous relay network, where two transceivers wish to communicate with the help of multiple multi-antenna relays. In an asynchronous network, the signal transmitted by any of the two transceivers arrives at different relays with significantly different delays. Similarly, the signals forwarded by different relays arrive at the receiver frontend of any of the two transceivers with different delays. In such a network, aiming to minimize the total transmit power consumed in the entire network, we obtain the relay beamforming matrices and the transceivers' transmit powers such that two given date rates at the two transceivers are to be satisfied. We develop a model for the end-to-end channel and use this model to address the network beamforming problem. Assuming symmetric relay beamforming matrices, we present a computationally efficient solution to this problem. The numerical results show that for a given total number of antennas, there is an optimal number of antennas per relays which results in the lowest total power consumption in the entire network.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130050087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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